Perfecting Liquid-State Theories with Machine Intelligence
This is an incremental perspective on advancing liquid-state theories for materials and chemical research.
The paper discusses leveraging functional machine learning to improve liquid-state theories, aiming to enhance accuracy, scalability, and computational efficiency for broader applications in materials and chemical systems.
Recent years have seen a significant increase in the use of machine intelligence for predicting electronic structure, molecular force fields, and the physicochemical properties of various condensed systems. However, substantial challenges remain in developing a comprehensive framework capable of handling a wide range of atomic compositions and thermodynamic conditions. This perspective discusses potential future developments in liquid-state theories leveraging on recent advancements of functional machine learning. By harnessing the strengths of theoretical analysis and machine learning techniques including surrogate models, dimension reduction and uncertainty quantification, we envision that liquid-state theories will gain significant improvements in accuracy, scalability and computational efficiency, enabling their broader applications across diverse materials and chemical systems.